--- title: "Caveman comparisons" description: "Caveman comparisons for LLM gateways, prompt compression, model routing, and agent observability. See where each tool fits and what changes when you add Caveman." canonical: https://caveman.so/compare last-updated: 2026-09-16 --- # Caveman comparisons Caveman comparisons for LLM gateways, prompt compression, model routing, and agent observability. See where each tool fits and what changes when you add Caveman. - [Caveman vs AgentOps: agent replays plus token compression](https://caveman.so/compare/agentops): AgentOps records and replays agent sessions and tracks cost. Caveman Middleware shrinks tool output before each model call. How they fit and what a span shows. - [Caveman vs Arize Phoenix: traces, evals and compression](https://caveman.so/compare/arize-phoenix): Arize Phoenix traces and evaluates LLM apps. Caveman Middleware shrinks tool output before the model call. How they fit, and which copy a Phoenix span records. - [Caveman vs Bifrost: fast AI gateway, fewer tool tokens](https://caveman.so/compare/bifrost): Bifrost adds microseconds to each LLM call. Caveman Middleware cuts the tool-result tokens inside the call. What each does and how to run both together. - [Caveman vs Braintrust: evals plus token compression](https://caveman.so/compare/braintrust): Braintrust scores and traces your AI app. Caveman Middleware shrinks the tool output your model calls send. How they fit and how to prove quality held. - [Caveman vs Cloudflare AI Gateway: add compression](https://caveman.so/compare/cloudflare-ai-gateway): Cloudflare AI Gateway gives you caching, rate limits and logs at the edge. Caveman Middleware compresses agent tool output before it gets there. Use both. - [Caveman vs Headroom: context compression compared](https://caveman.so/compare/headroom): Headroom and Caveman both compress agent context by content type and keep originals recoverable. Compare recovery, store lifetime, integrations and license. - [Caveman vs Helicone: LLM logs plus token compression](https://caveman.so/compare/helicone): Helicone logs, caches and routes LLM calls through its AI gateway. Caveman Middleware shrinks tool output before the call leaves your app. How the two fit. - [Caveman vs Langfuse: tracing plus token compression](https://caveman.so/compare/langfuse): Langfuse traces, evaluates and prices your LLM calls. Caveman Middleware shrinks the tool output those calls send. How to run both and check quality held. - [Caveman vs LangGraph: compress tool results in LangGraph](https://caveman.so/compare/langgraph): Keep LangGraph and add Caveman Middleware through the LangChain adapter: shrink large tool results in create_agent graphs, keep originals, recover on demand. - [Caveman vs LangSmith: traces, evals, fewer tokens](https://caveman.so/compare/langsmith): LangSmith traces and evaluates LangChain and other agents. Caveman Middleware cuts the tool-output tokens they send. What each does and how they work together. - [Caveman vs LiteLLM: tool output compression in LiteLLM](https://caveman.so/compare/litellm): Keep LiteLLM. Caveman Middleware runs inside it as a native callback and shrinks large tool results before LiteLLM sends them, with originals recoverable. - [Caveman vs LLMLingua: prompt compression compared](https://caveman.so/compare/llmlingua): LLMLingua prunes low-value tokens with a small model. Caveman compresses agent tool output by format and keeps every original recoverable. When to use each. - [Caveman vs Martian: model gateway, routing, compression](https://caveman.so/compare/martian): Martian Gateway gives one API over 200+ models, backed by routing research. See how it compares with Caveman Router and how Caveman compression runs through it. - [Caveman vs Mastra: token compression for Mastra agents](https://caveman.so/compare/mastra): Keep Mastra and add Caveman Middleware: withCavemanMastra wraps your agent, shrinks large tool results before each model call, and keeps originals recoverable. - [Caveman vs Not Diamond: AI model router comparison](https://caveman.so/compare/not-diamond): Not Diamond routes each prompt or agent step to the best-value model. Compare it with Caveman Router, and see how Caveman compression cuts tokens on any route. - [Caveman vs OpenRouter: compression and auto routing](https://caveman.so/compare/openrouter): OpenRouter gives one API for hundreds of models plus an Auto Router. Caveman Middleware cuts the tool-output tokens you send through it. What each one does. - [Caveman vs Portkey: AI gateway plus tool-token compression](https://caveman.so/compare/portkey): Portkey governs and observes your LLM traffic. Caveman Middleware shrinks large tool results before requests reach it. How they fit, and when to add it. - [Caveman vs PromptLayer: prompt registry plus compression](https://caveman.so/compare/promptlayer): PromptLayer versions, ships and evaluates prompts. Caveman Middleware shrinks the tool output around them. How they fit, and which copy PromptLayer logs. - [Caveman vs Pydantic AI: token compression for agents](https://caveman.so/compare/pydanticai): Keep Pydantic AI and add Caveman Middleware as a capability: CavemanCapability shrinks large tool returns before each model request, originals recoverable. - [Caveman vs RouteLLM: open-source LLM router compared](https://caveman.so/compare/routellm): RouteLLM is an open-source framework that routes between a strong and a weak model. Compare it with Caveman Router, and add Caveman compression to routed calls. - [Caveman vs RTK (Rust Token Killer): which to use](https://caveman.so/compare/rtk): RTK filters shell command output before your coding agent reads it. Caveman compresses every tool result and keeps originals recoverable. Where each one wins. - [Caveman vs Vercel AI Gateway: fewer tokens per call](https://caveman.so/compare/vercel-ai-gateway): Vercel AI Gateway gives one key, budgets and fallbacks across 200+ models. Caveman Middleware wraps the AI SDK model to shrink tool output. How they fit. - [Caveman vs Vercel AI SDK: tool-result compression](https://caveman.so/compare/vercel-ai-sdk): Keep the Vercel AI SDK and add Caveman Middleware: a native withCaveman adapter that shrinks large tool results before each model call, originals recoverable.